Tan Nguyen Publishes a Paper Making a Meaningful Contribution to the Field of Computer Science

Overview

A new open-access dataset with high practical value for precision agriculture has just been published in Data in Brief (ScienceDirect). The dataset focuses on durian, one of Vietnam’s most economically important fruit crops, and aims to support research and applications of artificial intelligence (AI) for real-field plant disease diagnosis.

Dataset Overview

• Title: Image Dataset of Ten Durian Diseases Captured in Real-Field Conditions from a Family Orchard in Vinh Long, Vietnam
• Location: A family-owned durian orchard and neighboring farms in Vinh Long Province, Vietnam
• Publication: 2025, Data in Brief (Elsevier)
• Number of images: 5,452 disease images
• Plant parts: Leaves, flowers, branches, trunks, and roots
• Capture device: iPhone 14 under natural field conditions

Key characteristics

• Images taken in real orchards
• Diverse lighting conditions and backgrounds
• Natural noise such as soil, weeds, and shadows

• Format: PNG images, preprocessed and ready for machine learning
• Data availability: Publicly available on Mendeley Data for research and development purposes

Why Is This Dataset Important?

Unlike many existing agricultural image datasets that are collected in controlled laboratory environments, this dataset was captured directly in real orchards. This makes it particularly valuable for training deep learning models that can perform reliably in real-world farming conditions.

The dataset closely reflects how farmers actually take photos using smartphones in the field, improving the robustness and real-life applicability of AI-based plant disease detection systems.

Dataset Details and Image Processing

Disease categories

• Covers 10 common durian diseases
• Each disease class contains approximately 405–427 original images

Image acquisition

• Photos were taken from multiple angles and distances
• Lighting conditions vary naturally throughout the day
• Backgrounds are complex and unstructured

Preprocessing

• Images were manually reviewed and cropped to focus on disease symptoms
• Converted to PNG format for consistency and compatibility
• Images can be resized (e.g., 224 × 224 pixels) depending on model requirements

This dataset can be used for

• Training AI models for automatic durian disease classification
• Developing mobile applications for on-site disease diagnosis
• Research in computer vision and agricultural AI
• Studying image classification under noisy, real-world conditions

It is especially suitable for projects aiming to deploy AI solutions directly to farmers and agricultural practitioners.

Tan Nguyen, 2025 — https://www.sciencedirect.com/science/article/pii/S2352340925009655